Iranian Journal of Electrical and Electronic Engineering

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In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribution of output error caused by s-a-0 (stuck at 0) faults in a MLP network has a Gaussian distribution function. UDBP (Uniformly Distributed Back Propagation) algorithm is then introduced to minimize mean and variance of the output error. Simulation results show that UDBP has the least sensitivity and the highest fault tolerance among other algorithms such as WRTA, N-FTBP and ADP. Then a MLP neural network trained with UDBP, contributes in an Algorithm Based Fault Tolerant (ABFT) scheme to protect a nonlinear data process block. The neural network is trained to produce an all zero syndrome sequence in the absence of any faults. A systematic real convolution code guarantees that faults representing errors in the processed data will result in notable nonzero values in syndrome sequence. A majority logic decoder can easily detect and correct single faults by observing the syndrome sequence. Simulation results demonstrating the error detection and correction behavior against random s-a-0 faults are presented too.

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In an environment such as underwater channel where placing test equipments are difficult to handle, it is much practical to have hardware simulators to examine suitably designed transceivers (transmitter/receiver). The simulators of this kind will then allow researchers to observe their intentions and carry out repetitive tests to find suitable digital coding/decoding algorithms. In this paper, a simplified shallow water digital data transmission system is first introduced. The transmission channel considered here is a stochastic DSP hardware model in which signal degradations leads to a severe distortion in phase and amplitude (fades) across the bandwidth of the received signal. A computer base-band channel model with frequency non-selective feature is derived by the authors [10-11]. This system was based on fullraised cosine channel modelling and proved to be the most suitable for vertical and shortrange underwater communication csdfher), with a reflected path (specula component, when the acoustic hydrophone receives reflected signals from surface and bottom of the sea) and a random path (diffused component, when the acoustic hydrophone receives scattered signals from the volume of the sea). The model assumed perfect transmitter-receiver synchronization but utilized realistic channel time delays, and demonstrated the timevarying characteristics of an underwater acoustic channel observed in practice. In this paper, they are used to provide a full system simulation in order to design an adaptive receiver employing the most advanced digital signal processing techniques in hardware to predict realizable error performances.

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A new fuzzy maximum power point tracker (MPPT) for photovoltaic systems is proposed. Fuzzy controller input parameters dI dP , ) dI dP ( D and variation of duty cycle ( DC D ) are used to generate the optimal MPPT converter duty cycle, such that solar panel maximum power is generated under different operating conditions. A photovoltaic system including a solar panel, a fuzzy MPP tracker and a resistive load is designed, simulated and constructed. The fuzzy MPP tracker includes a buck dc/dc converter, fuzzy controller and interfacing circuits. Theoretical and experimental results are used to indicate the advantages and limitations of the proposed technique.

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In this paper altitude measurement from water surface using laser beam is presented. Research data indicate that the reflection of infrared waves from water surface is about zero and it is less than 2% for visible radiations. Phase-shift and heterodyne technique was used for the measurement, and the laser beam ( mW p nm 10 , 700 = = l ) was modulated by a sine wave having a fixed frequency. The optimum design and low-noise elements made it possible to detect a light power about 20 nW at operating frequency.

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Motion estimation and compensation is an essential part of existing video coding systems. The mesh-based motion estimation (MME) produces smoother motion field, better subjective quality (free from blocking artifacts), and higher peak signal-to-noise ratio (PSNR) in many cases, especially at low bitrate video communications, compared to the conventional block matching algorithm (BMA). However, the iterative refinement process of MME is computationally much costly and makes the method impractical in real- (or near real-) time systems. Also, eliminating the iterative refinement step deteriorates the motion estimation result. In this paper, we propose motion adaptive interpolation schemes for noniterative MME, which use BMA to compute the motion vectors (MVs) of mesh nodes. The proposed algorithm aims at compromising the MME and BMA by modifying the interpolation patterns (IPPs) of the MME in an adaptive manner, based on the MVs of mesh nodes. Experimental results show notable rate-distortion improvement over both BMA and conventional non-iterative MME, with acceptable visual quality and system complexity, especially when applied to sequences with medium to high motion activities.

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In this paper a new method for determining the search area for motion estimation algorithm based on block matching is suggested. In the proposed method the search area is adaptively found for each block of a frame. This search area is similar to that of the full search (FS) algorithm but smaller for most blocks of a frame. Therefore, the proposed algorithm is analogous to FS in terms of regularity but has much less computational complexity. To find the search area, the temporal and spatial correlations among the motion vectors of blocks are used. Based on this, the matched block is chosen from a rectangular area that the prediction vectors set out. Simulation results indicate that the speed of the proposed algorithm is at least 7 times better than the FS algorithm.

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The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experimentally that the proposed IRGA-classifier has removed two important weak points of the conventional GA-classifiers. These problems are the large number of training points and the large number of iterations to achieve a comparable performance with the Bayes classifier, which is an optimal conventional classifier. Three examples have been chosen to compare the performance of designed IRGA-classifier to conventional GA-classifier and Bayes classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The results show clearly a considerable improvement for the performance of IRGA-classifier compared with a conventional GA-classifier.

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A robust skin detector is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography filtering. Less than 10 years ago, the first paper on automatic pornography filtering was published. Since then, different researchers claim different color spaces to be the best choice for skin detection in pornography filtering. Unfortunately, no comprehensive work is performed on evaluating different color spaces and their performance for detecting naked persons. As such, researchers usualy refer to the results of skin detection based on the work doen for face detection, which underlies different imaging conditions. In this paper, we examine 21 color spaces in all their possible representations for pixel-based skin detection in pornographic images. Consequently, this paper holds a large investigation in the field of skin detection, and a specific run on the pornographic images.

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Microarray technology is a new and powerful tool for concurrent monitoring of large number of genes expressions. Each microarray experiment produces hundreds of images. Each digital image requires a large storage space. Hence, real-time processing of these images and transmission of them necessitates efficient and custom-made lossless compression schemes. In this paper, we offer a new architecture for lossless compression of microarray images. In this architecture, we have used a dedicated hardware for separation of foreground pixels from the background ones. By separating these pixels and using pipeline architecture, a higher lossless compression ratio has been achieved as compared to other existing methods

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In this paper, thermal effects of laser irradiance on biological tissue is investigated using computer simulations. Earlier attempts in this field made use of finite difference and finite element techniques. Here a novel approach is adopted to improve the results. The effect of our implicit approach on the convergence procedure and accuracy of results, with different timing steps, is explored. Monte Carlo method is used in combination with the finite volume algorithm in order to obtain a profile of light distribution and heat transport in tissue. It is shown that implicit finite volume technique has not only acceptable accuracy, but also high stability for different timing steps.

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Medium-term modeling of electricity market has essential role in generation expansion planning. On the other hand, uncertainties strongly affect modeling and consequently, strategic analysis of generation firms in the medium term. Therefore, models considering these uncertainties are highly required. Among uncertain variables considered in the medium term generation planning, demand and hydro inflows are of the greatest importance. This paper proposes a new approach for simulating the operation of power market in medium-term, taking into account demand and hydro inflows uncertainties. The demand uncertainty is considered using Monte-Carlo simulations. Standard Deviation over Expected Profit (SDEP) of generation firms based on simulation results is introduced as a new index for analyzing the influence of the demand uncertainty on the behavior of market players. The correlation between capacity share of market players and their SDEP is also demonstrated. The uncertainty of inflow as a stochastic variable is dealt using scenario tree representation. Rational uncertainties as strategic behavior of generation firms, intending to maximize their expected profit, is considered and Nash-Equilibrium is determined using the Cournot model game. Market power mitigation effects through financial bilateral contracts as well as demand elasticity are also investigated. Case studies confirm that this representation of electricity market provides robust decisions and precise information about electricity market for market players which can be used in the generation expansion planning framework.

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Contrast resolution and detail resolution are two important parameters in ultrasound imaging. This paper presents a new method to enhance these parameters, simultaneously. A parallel auxiliary beamformer has been employed whose weightings are such that an estimation of the leaked signal through the main beamformer is obtained. Then the output of main beamformer is modified according to the estimated leaked signal. The efficiency of our adaptive method is demonstrated by applying it over an experimental data set and provided an enhancement of about 22 percent in lateral resolution and 15-20 dB in contrast resolution. This method also has the advantages of simplicity and possibility of real time implementation.

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Intruders often combine exploits against multiple vulnerabilities in order to break into the system. Each attack scenario is a sequence of exploits launched by an intruder that leads to an undesirable state such as access to a database, service disruption, etc. The collection of possible attack scenarios in a computer network can be represented by a directed graph, called network attack graph (NAG). The aim of minimization analysis of network attack graphs is to find a minimum critical set of exploits that completely disconnect the initial nodes and the goal nodes of the graph. In this paper, we present an ant colony optimization algorithm, called AntNAG, for minimization analysis of large-scale network attack graphs. Each ant constructs a critical set of exploits. A local search heuristic has been used to improve the overall performance of the algorithm. The aim is to find a minimum critical set of exploits that must be prevented to guarantee no attack scenario is possible. We compare the performance of the AntNAG with a greedy algorithm for minimization analysis of several large-scale network attack graphs. The results of the experiments show that the AntNAG can be successfully used for minimization analysis of large-scale network attack graphs.

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Hunting is a flutter associated with the synchronous speed that gives rise to the gyro drifting errors and may cause objectionable time-displacement errors in video head wheel drives and other precision scanning systems. In this paper, dynamic characteristics of permanent Magnet hysteresis motors are presented and hunting is explained. New damping techniques have been developed using optimized eigenvalues calculation. They are calculated from LQR optimization method. In this damping method, a distinct reduction in hunting has been archived. Furthermore field oriented control result of motor is presented that have good effect on Hunting. Nearest agreement between simulated and measurement results shows the accuracy of motor model. Comparison between this paper results and other measured damping methods result are shown its success.

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This paper presents a parametric low differential order model, suitable for mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous–time model with discrete measurements for fault detection ends. Typical motor faults as interturn short circuit and increased winding resistance are taken into account. The models are validated against winding function induction motor modeling which is well known in machine modeling field. The validation shows very good agreement between proposed method simulations and winding function method, for short-turn stator fault detection.

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Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transform modeling, and finally image resampling. As the accuracy of a registration process is highly dependent to the feature extraction and matching methods, in this paper, we have proposed a new method for extracting salient edges from satellite images. Due to the efficiency of multiresolution data representation, we have considered four state-of-the-art multiresolution transforms –namely, wavelet, curvelet, complex wavelet and contourlet transform- in the feature extraction step of the proposed image registration method. Experimental results and performance comparison among these transformations showed the high performance of the contourlet transform in extracting efficient edges from satellite images. Obtaining salient, stable and distinguishable features increased the accuracy of the proposed registration process.

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A mathematical model based on d-q axis theory and dynamic performance characteristic of brushless resolvers is discussed in this paper. The impact of rotor eccentricity on the accuracy of position in precise applications is investigated. In particular, the model takes the stator currents of brushless resolver into account. The proposed model is used to compute the dynamic and steady state equivalent circuit of resolvers. Finally, simulation results are presented. The validity and usefulness of the proposed method are thoroughly verified with experiments.

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This paper presents a novel optimization based methodology to allocate Flexible AC Transmission Systems (FACTS) devices in an attempt to improve the previously mentioned researches in this field. Static voltage stability enhancement, voltage profile improvement, line congestion alleviation, and FACTS devices investment cost reduction, have been considered, simultaneously, as objective functions. Therefore, multi-objective optimization without simplification has been used in this paper to find a logical solution to the allocation problem. The optimizations are carried out on the basis of location, size and type of FACTS devices. Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are utilized to achieve the determined objectives. The problem is formulated according to Sequential Quadratic Programming (SQP) problem in the first stage. This formulation is used to accurately evaluate static security margin with congestion alleviation constraint incorporating voltage dependence of loads in the presence of FACTS devices and estimated annual load profile. The best trade-off between conflicting objectives has been obtained through Genetic Algorithm (GA) based fuzzy multi-objective optimization approach, in the next stage. The IEEE 14-bus test system is selected to validate the allocated devices for all load-voltage characteristics determined by the proposed approach.

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The throughput enhancement of Space-Time Spreading (STS)-based Multicarrier Direct Sequence Code Division Multiple Access (MC DS-CDMA) system is investigated in this paper. Variable Spreading Factor (VSF) is utilized to improve the data throughput of the system. In this contribution, an analytical approach is proposed to compute a new expression for the Bit Error Rate (BER) performance of the STS-based MC DS-CDMA system against pre-dispreading Signal-to-Noise Ratio (SNR) for different values of spreading factor (SF). The other contribution of the paper is deriving a new closed form expression for computing the throughput enhancement and the BER performance of the VSF STS-based MC DS-CDMA system over Rayleigh fading channel. It is demonstrated that using VSF method in STS-based MC DS-CDMA system improves the throughput of the system by keeping the BER performance at the target level.

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The main theme of this paper is to present novel controller, which is a genetic based fuzzy Logic controller, for interior permanent magnet synchronous motor drives with direct torque control. A radial basis function network has been used for online tuning of the genetic based fuzzy logic controller. Initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition, a genetic algorithm is used to optimize fuzzy logic parameters in closed-loop direct torque control scheme. In other words, the genetic algorithm finds optimum input and output scaling factors and optimum number of membership functions. This optimization procedure is utilized to obtain the minimum speed deviation, minimum settling time, zero steady-state error. The control scheme has been verified by simulation tests with a prototype interior permanent magnet synchronous motor.